Quantitative expression and localization of GABAB receptor protein subunits in hippocampi from patients with refractory temporal lobe epilepsy

This study investigates GABAB protein expression and mRNA levels in three types of specimens. Two types of specimens from patients with temporal lobe epilepsy (TLE), secondary to hippocampal sclerosis, sclerotic hippocampal samples (TLE-HS), and tissue from the structurally preserved non-spiking ipsilateral superior temporal gyrus (TLE-STG) removed from the same patient during epilepsy surgery; and third specimen is hippocampal tissue from individuals with no history of epilepsy (post-mortem controls, PMC). mRNA expression of GABAB subunits was quantified in TLE-HS, TLE-STG and PMC specimens by qRT-PCR. Qualitative and quantitative Western blot (WB) and immunohistochemistry techniques were employed to quantify and localize GABAB proteins subunits. qRT-PCR data demonstrated an overall decrease of both GABAB1 isoforms in TLE-HS compared to TLE-STG. These results were mirrored by the WB findings. GABAB2 mRNA and protein were significantly reduced in TLE-HS samples compared to TLE-STG; however they appeared to be upregulated in TLE-HS compared to the PMC samples. Immunohistochemistry (IHC) showed that GABAB proteins were widely distributed in PMC and TLE-HS hippocampal sections with regional differences in the intensity of the signal. The higher expression of mature GABAB protein in TLE-HS than PMC is in agreement with previous studies. However, these findings could be due to post-mortem changes in PMC specimens. The TLE-STG samples examined here represent a better 'control' tissue compared to TLE-HS samples characterized by lower than expected GABAB expression. This interpretation provides a better explanation for previous functional studies suggesting reduced inhibition in TLE-HS tissue due to attenuated GABAB currents. [Abstract copyright: Copyright © 2017. Published by Elsevier Ltd.

Task-specificity in focal dystonia is shaped by aberrant diversity of a functional network kernel

OBJECTIVES: Task-specific focal dystonia selectively affects the motor control during skilled and highly learned behaviors. Recent data suggest the role of neural network abnormalities in the development of the pathophysiological dystonic cascade. METHODS: We used resting-state functional MRI and analytic techniques rooted in network science and graph theory to examine the formation of abnormal subnetwork of highly influential brain regions, the functional network kernel, and its influence on aberrant dystonic connectivity specific to affected body region and skilled motor behavior. RESULTS: We found abnormal embedding of sensorimotor cortex and prefrontal thalamus in dystonic network kernel as a hallmark of task-specific focal dystonia. Dependent on the affected body region, aberrant functional specialization of the network kernel included regions of motor control management in focal hand dystonia (writer's cramp, musician's focal hand dystonia) and sensorimotor processing in laryngeal dystonia (spasmodic dysphonia, singer's laryngeal dystonia). Dependent on skilled motor behavior, the network kernel featured altered connectivity between sensory and motor execution circuits in musician's dystonia (musician's focal hand dystonia, singer's laryngeal dystonia) and abnormal integration of sensory feedback into motor planning and executive circuits in non-musician's dystonia (writer's cramp, spasmodic dysphonia). CONCLUSIONS: Our study identified specific traits in disorganization of large-scale neural connectivity that underlie the common pathophysiology of task-specific focal dystonia while reflecting distinct symptomatology of its different forms. Identification of specialized regions of information transfer that influence dystonic network activity is an important step for future delineation of targets for neuromodulation as a potential therapeutic option of task-specific focal dystonia. (c) 2018 International Parkinson and Movement Disorder Society

A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number of extensions of the proposed methodology building upon the current model

J Comp Neurol

Although the concept of left-hemispheric lateralization of neural processes during speech production has been known since the times of Broca, its physiological underpinnings still remain elusive. We sought to assess the modulatory influences of a major neurotransmitter, dopamine, on hemispheric lateralization during real-life speaking using a multimodal analysis of functional MRI, intracranial EEG recordings, and large-scale neural population simulations based on diffusion-weighted MRI. We demonstrate that speech-induced phasic dopamine release into the dorsal striatum and speech motor cortex exerts direct modulation of neuronal activity in these regions and drives left-hemispheric lateralization of speech production network. Dopamine-induced lateralization of functional activity and networks during speaking is not dependent on lateralization of structural nigro-striatal and nigro-motocortical pathways. Our findings provide the first mechanistic explanation for left-hemispheric lateralization of human speech that is due to left-lateralized dopaminergic modulation of brain activity and functional networks. This article is protected by copyright. All rights reserved

Personalized Medicine : Optimization of EPO Dosing

The project concerns the use of optimal control methods for computation of EPO (Erythropoietin) doses in hemodialysis patients.publishe

(A) Empirical and simulated functional networks in the resting state and during speech production and (B) nodal strength for experimental (left column) and simulated (right column) functional networks in resting state (gray) and during speech production (red).

<p>(A) 3D visualizations of data- and model-based NMI networks (top and bottom rows, respectively) during rest (left column) and speech production (right column). Edge colors represent NMI coefficient values and nodal color illustrates strength (normalized to the interval ). (B) Nodal strength of data- and model-based NMI networks. The top row shows the nodal strength per node, the bottom row illustrates the distribution of <i>s_i</i>-values. The 3D networks were visualized with the BrainNet Viewer (<a href="http://www.nitrc.org/projects/bnv/" target="_blank">http://www.nitrc.org/projects/bnv/</a>). <i>Abbreviations</i>: MFG  =  middle frontal gyrus, Cu  =  cuneus, FP  =  frontal pole, FG  =  fusiform gyrus, IPC/SPC  =  inferior/superior parietal cortex, LMC  =  laryngeal motor cortex, OC  =  occipital cortex, PreCG  =  precentral gyrus, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, PostCG  =  postcentral gyrus, STC  =  superior temporal cortex, mFG  =  medial frontal gyrus, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus.</p

All parameters used in the model (including the neural and dopamine components) are provided according to their notation used in the paper, with their description, their value, and their basic units.

<p><i>Abbreviations</i>: mV  =  Millivolt, mS  =  Millisiemens, ms  =  Millisecond, mM  =  Millimole, kHz  =  Kilohertz.</p><p>All parameters used in the model (including the neural and dopamine components) are provided according to their notation used in the paper, with their description, their value, and their basic units.</p

Non-normalized and normalized segregation and integration metrics for experimental and simulated functional networks in resting state (gray) and during speech production (red).

<p>Distributions of (A) non-normalized clustering coefficient, (B) non-normalized local efficiency, (C) normalized clustering coefficient, and (D) normalized local efficiency in the data- and model-based NMI networks.</p

Simulated and empirical BOLD signal during (A) rest and (B) speech and NMI matrices of (C) data and (D) model in resting state and during speech production.

<p>The colored lines show time courses of simulated BOLD signals during resting state (A) and for dopamine modulation (B) for regions of the brain associated with speech production. Experimental BOLD time courses are shown in gray. The labels ‘left’ and ‘right’ indicate left and right hemispheres respectively. Pairwise interactions within the signals were quantified by computing NMI coefficients for each pair of regional time-series corresponding to the simulated and real BOLD time-courses. This gave rise to four NMI-matrices (pairwise interactions of data (C) and model (D) in the resting state and during speech production). Because a normalized variant of the mutual information was employed, all matrix entries were bounded by zero and one. The parcellated brain regions used for the construction of matrices are provided in top (C) for both left and right hemispheres; the magnified inset shows the brain regions per hemisphere. <i>Abbreviations</i>: ACC/ICC/MCC/PCC  =  anterior/isthmus/middle/posterior cingulate cortex, Cu/PCu  =  cuneus/precuneus, ETC  =  entorhinal cortex, FG  =  fusiform gyrus, FP  =  frontal pole, IFGop/IFGor/IFGtr  =  pars opercularis/pars orbitalis/pars triangularis of the inferior frontal gyrus, IPC/SPC  =  inferior/superior parietal cortex, ITC/STC  =  inferior/superior temporacl cortex, LG  =  lingual gyrus, LMC  =  laryngeal motor cortex, LOFC/MOFC  =  lateral/medial orbitofrontal cortex, MFG  =  middle frontal gyrus, mFG  =  medial frontal gyrus, MTG  =  middle temporal gyrus, OC  =  occipital cortex, PCAC  =  pericalcerine cortex, PHip  =  parahippocampal cortex, PreCG/PostCG  =  pre/postcentral gyrus, Put  =  putamen, SFG  =  superior frontal gyrus, SMG  =  supramarginal gyrus, SNc  =  substantia nigra pars compacta, TP  =  temporal pole, TTC  =  transverse temporal cortex, Th  =  thalamus.</p